Technological prospects of Belt and Road Initiative in Bangladesh.

This study provides an alternative agenda to better explain the Belt and Road Initiative's (BRI's) technological connotations in Bangladesh using the Game Theory and Demand Curve approaches. BRI can proceed as a means to technology development for Bangladesh based on foreign direct investment (FDI) spillover effects that ranked China as the top FDI source, with 1159.42 million USD invested in 2018-2019. The findings suggest that motivated by mutual interests of economic transformation (China) and technological requirements (Bangladesh), BRI offers a bargaining game of cooperation. Thus, while economic transformation may force China to relocate its garment factories, Bangladesh's low wages and geopolitical location give it a superior position regarding relocation. The technological effects of such relocation will be two-fold: exchanges of tacit knowledge (conventional) and techno-based infrastructural support (component) that align with the proposed technology development framework on a macro level. More conventional technological projects and additional sector-based technology transfer are required to amplify BRI's technological forecasts. Moreover, to encourage more abundant FDI, bank loan interest must be decreased, and political stability has to be ensured. Both survey-based fieldwork and projects-based qualitative research need to be conducted to discover BRI's tangible technological implications.

A multi-head residual connection GCN for EEG emotion recognition.

Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer interaction. However, conventional neural networks have limitations in extracting profound EEG emotional features. This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks. The decomposition of multi-band differential entropy (DE) features exposes the temporal intricacy of emotion-linked brain activity, and the combination of short and long-distance brain networks can explore complex topological characteristics. Moreover, the residual-based architecture not only enhances performance but also augments classification stability across subjects. The visualization of brain network connectivity offers a practical technique for investigating emotional regulation mechanisms. The MRGCN model exhibits average classification accuracies of 95.8% and 98.9% for the DEAP and SEED datasets, respectively, highlighting its excellent performance and robustness.